Ramon Vilanova

A New Frequency Dependent Approach to Model Validation

In order to have confidence in a model it is necessary to validate it. Different model validation approaches exist. Their difference is based upon the assumptions about the plant and models. Classical validation methods, based on classical model identification (Ljung, 1994; Soderstrom and Stoica, 1989) rely on statistical uncertainty assumptions due to stochastic noise only. On the other hand, control oriented identification methods (Chen and

Robustness in PID Control

Robustness should not be taken for granted. After decades of gaining acceptance, Robust Control Theory has permeated practically every approach to controller design. Of course, PID cannot be an exception. The introduction of robustness considerations within the PID paradigm has created an active focus of research. During the last decades, a number of approaches have emerged introducing new considerations into the design of PID controllers by considering the robustness as a design specification. Of course, this can take several forms and formulations. This chapter specially concentrates on those approaches that lead to robust tuning rules. The use of different robustness measures is presented, and the importance of ensuring the specifications are met. This fact allows for a posterior analysis of the well-known (but not quantitatively analyzed) Robustness/Performance tradeoff. A possible route to this analysis is presented at the end of the chapter as a suggestion on how to approach this problem and have a clearer idea of the price paid for increasing the demand for robustness.

Fragility Evaluation of PI and PID Controllers Tuning Rules

Currently, the design of Proportional Integral Derivative (PID) controllers must take into account the closed-loop control system performance to set-point and load-disturbance step changes, its robustness to the changes in the controlled process characteristics, the control effort smoothness and extreme requirements, and its fragility to the variation of the controller parameters. In this chapter, the early work on the controller fragility is revised and the Delta Epsilon Robustness and Performance Fragility Indices are presented as measurements of the loss of robustness and/or performance when the controller parameters change due to inaccuracies in its implementation or due to its final fine tuning. Using the Delta 20 Fragility Indices, the fragility index is defined when a controller may be considered as a robustness or performance resilient, non-fragile or fragile controller. The fragility of a tuning rule as a whole, its ability to produce robustness- and/or performance-non-fragile controllers, is evaluated using the fragility plots. It is shown that although a controller tuning rule can produce control systems with a constant nominal robustness over its entire range of application, the controller fragility is affected by the controlled process parameters and by the control algorithm implementation.

Balanced Performance/Robustness PID Design

The design of the closed-loop control system must take into account the system performance to load-disturbance and set-point changes and its robustness to variation of the controlled process characteristics, preserving the well-known trade-off among all these variables. This work faces with the combined servo/regulation performance and robustness problem, in order to get an intermediate solution between the robustness increase and the consequent loss in the optimality degree of the performance. The proposed balanced Proportional-Integrative-Derivative (PID) control design is tested against other tuning methods.

A Model Reference Based 2-DOF Robust Observer-Controller Design Methodology

Disregarding the sign, the reference r and the disturbance d have the same effect on the error e . Therefore, if r and d vary in a similar manner the controller K can be chosen to minimize e in some sense. Otherwise, if r and d have different nature, the controller has to be chosen to provide a good trade-off between the command tracking and the disturbance rejection responses. This compromise is inherent to the nature of 1-DOF control schemes. To allow independent controller adjustments for both r and d , additional controller blocks have to be introduced into the system as in figure 2. Two-degree-of-freedom (2-DOF) compensators are characterized by allowing a separate processing of the reference inputs and the controlled outputs and may be characterized by means of two stable Youla parameters. The 2-DOF compensators present the advantage of a complete separation between feedback and reference tracking properties (Youla & Bongiorno, 1985): the feedback properties of the controlled system are assured by a feedback Source: New Approaches in Automation and Robotics, Book edited by: Harald Aschemann, ISBN 978-3-902613-26-4, pp. 392, May 2008, I-Tech Education and Publishing, Vienna, Austria O pe n A cc es s D at ab as e w w w .in te hw eb .c om